Srinivas Narayanan takes a deep look into the next change we’re seeing in AI—going beyond fully supervised learning techniques.
Daniel Russakoff discusses how AI is being used to predict age-related macular degeneration progression.
Sarah Bird discusses the major challenges of responsible AI development and examines promising new tools and technologies to help enable it in practice.
Dinesh Nirmal examines how organizations can unlock the value of their data for AI with a unified, prescriptive information architecture.
Eric Gardner shares a four-step journey that all kinds of organizations can use to evaluate their unique paths from data to insights.
Andrew Feldman discusses the Wafer Scale Engine, the largest chip ever built.
A look at the landscape of tools for building and deploying robust, production-ready machine learning models.
To successfully implement AI technologies, companies need to take a holistic approach toward retraining their workforces.
We shouldn't ask our AI tools to be fair; instead, we should ask them to be less unfair and be willing to iterate until we see improvement.
Experts explore the future of hiring, AI breakthroughs, embedded machine learning, and more.
A look at how guidelines from regulated industries can help shape your ML strategy.
Tim Kraska outlines ways to build learned algorithms and data structures to achieve “instance optimality” and unprecedented performance for a wide range of applications.
Michael James examines the fundamental drivers of computer technology and surveys the landscape of AI hardware solutions.
Mikio Braun takes a look at Zalando and the retail industry to explore how AI is redefining the way ecommerce sites interact with customers.
Haoyuan Li offers an overview of a data orchestration layer that provides a unified data access and caching layer for single cloud, hybrid, and multicloud deployments.
Abigail Hing Wen discusses some of the most exciting recent breakthroughs in AI and robotics.
Ion Stoica outlines a few projects at the intersection of AI and systems that UC Berkeley's RISELab is developing.
Pete Warden digs into why embedded machine learning is so important, how to implement it on existing chips, and some of the new use cases it will unlock.